Forecasters also estimate bias by using a ______________, which is a ratio of therunning sum of forecast errors to the cumulative Mean Absolute…

The Tracking Signal is a performance metric used to monitor forecasts for bias. It is calculated as the ratio of the Running Sum of Forecast Errors (RSFE) to the Mean Absolute Deviation (MAD). A value consistently outside a predefined range (e.g., ±4 or ±5) indicates that the forecast is biased (i.e., consistently too high or too low), suggesting the forecasting model may need to be reviewed or adjusted. This metric helps distinguish between random error and a systematic bias in the forecasting process.

A. Key pointer: This is a generic, non-standard term in forecasting and supply chain management literature; it does not refer to a specific metric.

B. Single period method: This refers to an inventory model (like the newsvendor problem) for items with a single selling opportunity, not a technique for monitoring forecast bias over time.

D. Mean square error method: Mean Square Error (MSE) is a measure of forecast accuracy that averages the square of the errors. It is not the ratio of RSFE to MAD.

1. Chopra

S.

& Meindl

P. (2019). Supply Chain Management: Strategy

Planning

and Operation (7th ed.). Pearson. In Chapter 7

"Demand Forecasting in a Supply Chain

" the tracking signal is defined as a measure to track forecast error and is calculated as RSFE/MAD.

2. MIT OpenCourseWare. (2006). 15.762 Supply Chain Planning

Lecture 3: Forecasting. Massachusetts Institute of Technology. p. 26. Retrieved from https://ocw.mit.edu/courses/15-762-supply-chain-planning-spring-2006/resources/lecture3foreca/. The lecture notes explicitly define the Tracking Signal (TS) as: TS = RSFE / MAD.

3. Jacobs

F. R.

& Chase

R. B. (2018). Operations and Supply Chain Management (15th ed.). McGraw-Hill Education. Chapter 18

"Forecasting

" describes the tracking signal as a measurement of how well a forecast is predicting actual values and provides the formula: Tracking signal = Running sum of forecast errors / Mean absolute deviation.